TypeError: validation_step() takes 2 positional arguments but 3 were given

[chriswangky]

When running my model I get the error message:TypeError: validation_step() takes 2 positional arguments but 3 were given
Here is my code.

def val_dataloader(self):
        # expect to get train folder
        dataset = load_train_data(self.val_data_path)
        try:
            num_workers = multiprocessing.cpu_count()
        except:
            num_workers = 1
        dataloader = DataLoader(dataset, batch_size=1024, num_workers=num_workers, persistent_workers=True,
                                collate_fn=dataset_collate_function, shuffle=True)

        return dataloader

def validation_step(self, batch, batch_idx):
        x_app = batch['feature'].float()
        y_app = batch['app_label'].long()
        y_tra, y_all, index = drop_na(batch)
        y_hat_app, y_hat_tra, y_hat_all = self(x_app, index)

        app_f1 = F1Score(num_classes=17)
        tra_f1 = F1Score(num_classes=12)
        all_f1 = F1Score(num_classes=6)
        F1_app = app_f1(y_hat_app, y_app)
        F1_tra = tra_f1(y_hat_tra, y_tra)
        F1_all = all_f1(y_hat_all, y_all)
        F1_score = (F1_app + F1_tra + F1_all) / 3.0
        self.log('val_F1_app', F1_app, on_step=True, on_epoch=True)
        self.log('val_F1_tra', F1_tra, on_step=True, on_epoch=True)
        self.log('val_F1_all', F1_all, on_step=True, on_epoch=True)
        self.log('val_F1_score', F1_score, on_step=True, on_epoch=True)

        entropy_app = F.cross_entropy(y_hat_app, y_app)
        entropy_tra = F.cross_entropy(y_hat_tra, y_tra)
        entropy_all = F.cross_entropy(y_hat_all, y_all)
        loss_weight = self.app_weight + 2 + 1
        entropy = (self.app_weight * entropy_app + 2 *
                   entropy_tra + 1*entropy_all) / loss_weight
        self.log('val_app_loss', entropy_app, prog_bar=True,
                 logger=True, on_step=True, on_epoch=True)
        self.log('val_tra_loss', entropy_tra, prog_bar=True,
                 logger=True, on_step=True, on_epoch=True)
        self.log('val_all_loss', entropy_all, prog_bar=True,
                 logger=True, on_step=True, on_epoch=True)
        self.log('validation_loss', entropy, prog_bar=True,
                 logger=True, on_step=True, on_epoch=True)

        return {"val_loss": entropy, "val_F1score": F1_score}

Any advice on what happened?
Thank you very much!

[rohitgr7]

your code looks correct. Are you sure you are using a single validation dataloader?
if yes, can you reproduce it using BoringModel??

[chriswangky]

https://colab.research.google.com/drive/15BHeLJNYQpsiqE_1CpkucvPs45NCd3sl?usp=sharing

I updata the boringmodel.

[rohitgr7]

your validation step is different from what you shared above.

In the notebook it is

def validation_step(self, batch):

but it should be:

def validation_step(self, batch, batch_idx):

same for training_step.

[chriswangky]

Thank you! It solved my problem. But when I use training_step only, I defined def validation_step(self, batch):, and no problems occurred.
Why do I need batch_idx, when using training_step and validation_step both? (I don't use batch_idx in my code)

[rohitgr7]

it is generic. if you don't use it you can ignore but can be used in other use-cases.

[chriswangky]

OK, and thanks again!